Method and system for blind interference cancellation in a wireless communication systems

ABSTRACT

Aspects of the present invention include methods, systems, and computer-readable medium for canceling interference in wireless communication. The method includes receiving wireless CDMA communication signals using one or more antennas at least from a first entity via a first communication channel and a second entity via a second communication channel, determining a set of known characteristics associated with the first entity, the first set of characteristics comprising a first signal strength, a first synchronization information, and an first channel identification information, and determining an aggregate signal matrix based on signals received from at least the first entity and the second entity. The method further includes determining a covariance matrix associated with the aggregate signal value, determining a reference signal matrix based on the set of known characteristics, calculating an interference matrix by subtracting the reference signal matrix from the covariance matrix, and removing the interference estimation from the communication signals.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority to PCT application No.PCT/CN2012/084511, filed on Nov. 13, 2012, and entitled “Method andSystem for Blind Interference Cancellation in a wireless communicationsystems”, the entire disclosure of which is incorporated herein byreference.

BACKGROUND

Currently, there are two main types of interference cancellationreceivers. Namely, linear minimal mean square error (LMMSE) basedinterference cancellation receivers and non-linear interferencecancellation (NLIC) receivers. LMMSE based interference cancellationreceivers estimate the covariance matrix and find the coefficient whichis used to achieve the MMSE by solving the large matrix inversion. Theminimal required pre-knowledge to use this algorithm in CDMA basedcommunication systems is the desired signal's scrambling and spreadcode. The NIX based interference cancellation receivers estimateinterference items and subtract such items from the received signal inparallel or successively.

These techniques reconstruct the interference items by estimating eachinterference's amplitude, timing, channel response, and information bitand then remove them from the received signal, based on thepre-knowledge of the desired signal and interference's scrambling andspread codes. Specifically, in wide-band code division multiple access(CDMA) (WCDMA) downlink, the knowledge of variable spreading factors andspread codes of intra-channel and inter-channel interferences is verylimited, and an additional algorithm is needed to detect the presence ofthe interferences.

A significant drawback of these existing interference cancellationtechniques is that they require computational complexity and have highrequirement for the accuracy of the estimated interference signal in theNLIC receivers. Hence, for these and other reasons, improvements areneeded in the art.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the nature and advantages of the presentinvention may be realized by reference to the remaining portions of thespecification and the drawings wherein like reference numerals are usedthroughout the several drawings to refer to similar components. In someinstances, a sub-label is associated with a reference numeral to denoteone of multiple similar components. When reference is made to areference numeral without specification to an existing sub-label, it isintended to refer to all such multiple similar components.

FIG. 1 illustrates a block diagram of a receiver with blind interferencecancellation, according to one embodiment of the present invention.

FIG. 2 illustrates a block diagram of intra-interference andinter-interference cancellation and channel equalization, according toone embodiment of the present invention.

FIG. 3 illustrates a block diagram of blind interference cancellation oneach channel, according to one embodiment of the present invention.

FIG. 4 illustrates a block diagram of multi-channel/multi-stage blindinterference cancellation, according to one embodiment of the presentinvention.

FIGS. 5A and 5B illustrates flow diagrams for implementing blindinterference cancellation, according to one embodiment of the presentinvention.

FIG. 6 illustrates a block diagram of a receiver with an interferencecancellation and equalizer according to one embodiment of the presentinvention.

FIG. 7 illustrates a block diagram of multi-channel interferencecancellation and equalizer, according to one embodiment of the presentinvention.

FIG. 8 illustrates a block diagram of self-tracking multi channelequalizer based on a pilot channel, according to one embodiment of thepresent invention.

FIG. 9 illustrates a flow diagram for combining symbol levelinterference cancellation and chip level channel equalizer, according toone embodiment of the present invention.

FIG. 10 illustrates a flow diagram for dynamically selecting theinterference cancellation and channel equalizer, according to oneembodiment of the present invention.

FIG. 11 is a generalized schematic diagram illustrating a computersystem, in accordance with various embodiments of the invention.

FIG. 12 is a block diagram illustrating a networked system of computers,which can be used in accordance with various embodiments of theinvention.

SUMMARY OF THE INVENTION

The present invention is related to a method for canceling interferencein wireless communication. The method includes receiving wireless codedivision multiple access (CDMA) communication signals using one or moreantennas at least from a first entity via a first communication channeland a second entity via a second communication channel, determining aset of known characteristics associated with the first entity, the firstset of characteristics comprising a first signal strength, a firstsynchronization information, and an first channel identificationinformation, and determining aggregate signal matrix based on signalsreceived from at least the first entity and the second entity. Themethod further includes determining a covariance matrix associated withthe aggregate signal value, determining a reference signal matrix basedon the set of known characteristics, and calculating an interferencematrix by subtracting the reference signal matrix from the covariancematrix. Furthermore, the method includes determining a maximumeigenvalue and an eigenvector corresponding to the maximum eigenvaluefor with the interference matrix, generating an interference estimationusing the maximum eigenvalue and the eigenvector, and removing theinterference estimation from the communication signals.

The method also includes performing recursive calculations with athreshold condition to determine the maximum eigenvalue. The thresholdis determined based on a desired signal-to-noise ratio and thecorrelation feature of the designed signature code. Also, the channelidentification information comprises a signature (spreading) code,scrambling code, in orthogonal code, or non-orthogonal. The referencesignal matrix is associated with covariance values of the set of knowncharacteristics, and the wireless communication CDMA signals includesinterference signals.

In a further embodiment, a system for canceling interference in wirelesscommunication, is described. The system includes a storage device, acommon reference timing, and a processor in communication with thestorage device. There are two types of storage devices: instructionmemory block and data memory block. The former storage device has setsof instructions stored thereon which, when executed by the processor,cause the processor to: receive wireless code division multiple access(CDMA) communication signals using one or more antennas at least from afirst entity via a first communication channel and a second entity via asecond communication channel, called reference channel,and otherentities via all other available communication channels and save to thedata storage device at a preset time. The processor will process thesesaved data offline to: determine a set of known characteristicsassociated with the first entity, the first set of characteristicscomprising a first signal strength, a first synchronization information,and an first channel identification information, and determine anaggregate signal matrix based on signals received from at least thefirst entity and the other related entities.

The sets of instructions further cause the processor to determine acovariance matrix associated with the aggregate signal value, determinea reference signal matrix based on the set of known characteristics,calculate an interference matrix by subtracting the reference signalmatrix from the covariance matrix, determine a maximum eigenvalue and aneigenvector corresponding to the maximum eigenvalue for with theinterference matrix, generate an interference estimation using themaximum eigenvalue and the eigenvector, and remove the interferenceestimation from the communication signals.

Further, another embodiment describes a hardware processing acceleratorto complete all the above interference estimation and cancellationprocess. Further, another embodiment describes a computer-readablemedium for updating user assistance content. The computer-readablemedium has sets of instructions stored thereon which, when executed by acomputer, cause the computer to: receive wireless code division multipleaccess (CDMA) communication signals using one or more antennas at leastfrom a first entity via a first communication channel, a second entityvia a second communication channel and so on, determine a set of knowncharacteristics associated with the first entity, the first set ofcharacteristics comprising a first signal strength, a firstsynchronization information, and an first channel identificationinformation, determine an aggregate signal matrix based on signalsreceived from at least the first entity and the second entity, determinea covariance matrix associated with the aggregate signal value,determine a reference signal matrix based on the set of knowncharacteristics, calculate an interference matrix by subtracting thereference signal matrix from the covariance matrix, determine a maximumeigenvalue and an eigenvector corresponding to the maximum eigenvaluefor with the interference matrix, generate an interference estimationusing the maximum eigenvalue and the eigenvector, and remove theinterference estimation from the communication signals.

DETAILED DESCRIPTION OF THE INVENTION

While various aspects of embodiments of the invention have beensummarized above, the following detailed description illustratesexemplary embodiments in further detail to enable one of skill in theart to practice the invention. In the following description, for thepurposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the present invention. Itwill be apparent, however, to one skilled in the art that the presentinvention may be practiced without some of these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form. Several embodiments of the invention are described belowand, while various features are ascribed to different embodiments, itshould be appreciated that the features described respect to oneembodiment may be incorporated with another embodiment as well. By thesame token, however, no single feature or features of any describedembodiment should be considered essential to the invention, as otherembodiments of the invention may omit such features.

Aspects of the present invention relate to inter symbol interferencecancellation and channel equalizer. Removing interferences includessymbol level inter-interference and intra-interference cancellation(e.g., blind interference cancellation). Aspects of the inventionfurther includes chip level inter symbol interference cancellation andchannel equalization, time tracking on the reference channel byanalyzing the equalizer channel profile, estimate the equalizercoefficient on the reference channel, applying the equalizer coefficienton all channels associated with the desired user, and dynamicallyselecting the combination of the symbol level inter-interference andintra-interference cancellation and chip level equalizer based on theinterference detection and the output signal-to-noise ratio.

Further aspects of the present invention relate to interferencecancellation of wireless communication systems, and specifically to CDMAbased mobile communication systems (i.e., WCDMA) which are particularlyapplied in a universal mobile telecommunications system (UMTS)terrestrial radio access-frequency division duplexing (UTRA-FDD)downlink receiver to cancel the intra-cell interference, and inter-cellinterference, and mitigate inter symbol interference (ISI) interference.

Instead of estimating an interference signal, the present inventionblindly cancels interferences in succession based on second orderstatistics to avoid the computational complexity. The most dominantinterference contribution to the received covariance matrix is firstestimated and then removed from the receiving vector. Then, the weakerinterferences are removed in succession by repeating this process. Thedesired signal will be detected after each of the significantinterference items are removed. The estimation of the interferencecontribution to the received covariance matrix can be implemented byusing subspace technique to project the interference on the maximumeigenvalue and its corresponding maximum eigenvector.

Furthermore, the present invention provides improved performance byadding the interference cancellation and thus increasing the data rate.Further, the effect of signal covariance matrix estimation errors aremitigated. The computational complexity is decreased by removing theblock to detect unknown interferences. Processing delay is reduced byremoving the interference reconstruction from the decision feedback. Assuch, the present invention increases calculation speed withoutsacrificing accuracy in order to solve inter-cellular and intra-cellularinterferences.

Referring now to FIG. 1 which a system 100 of a receiver with blindinterference cancellation, according to one embodiment of the presentinvention. In one embodiment, the system 100 includes a radio frequencyblock 101 in communication with a digital front end block 102. Radiofrequency block 101 includes an RF block A 103 and RF block B 104, whichreceive radio frequencies Rx1 and Rxi, respectively.

Digital front end block 102 includes a digital sampling block A 105which receives input from RF block A 103 and a digital sampling block B106 which receives input from RF block B 104. Further, digital front endblock 102 includes a storage block A 107, a channel estimation block A109, a storage block B 108, and a channel estimation block B 110.Storage block A 107, channel estimation block A 109 receive inputs fromdigital sampling block A 105, and storage block B 108, channelestimation block B 110 receive inputs from digital sampling block B 106.Accordingly, storage block A 107 and storage block B 108 store thereceived signals, and channel estimation block A 109 and channelestimation block B 110 perform channel estimation based on the receivedsignals.

A scrambling spreading code generator 111 is in communication withchannel estimation block A 109 and channel estimation block B 110. Then,a blind interference cancellation block A 112 receives {r1} from storageblock A 107, {h1} from channel estimation block A 109, and an input fromscrambling spreading code generator 111. Further, a blind interferencecancellation block B 113 receives {ri} from storage block B 108, {hi}from channel estimation block B 110, and an input from scramblingspreading code generator 111. Furthermore, a RF block 114 receives inputfrom blind interference cancellation block A 112, blind interferencecancellation block B 113, and {sci} from scrambling spreading codegenerator 111.

Turning now to FIG. 2, which is block diagram illustrating a system 200for implementing intra-interference and inter-interference cancellationand channel equalization, according to one embodiment of the presentinvention. In one embodiment, system 200 includes a m-stage intra-CHkblind interference cancellation (BIC)1 201, an m-stage intra-CH1 BIC1202, and a m-stage CPICH BIC1 203, each of which receive input fromr⁽¹⁾. Furthermore, m-stage intra-CHk BIC2 206, an m-stage intra-CH1 BIC2205, and a m-stage CPICH BIC2 204, each of which receive input fromr⁽²⁾.

In a further embodiment, system 200 includes an equalizer 211 whichincludes a channel equalizer (CE)1 207 in communication with an equation(EQU)1 coefficient (COEFF) 209, and a CE2 208 in communication with anEQU2 COEFF 210. Further, CE1 207 receives input r_(m,pi) ⁽¹⁾ fromm-stage CPICH BIC1 203, and CE2 208 receives input r_(m,pi) ⁽²⁾ fromm-stage CPICH BIC1 204.

System 200 further includes an apply EQU coefficients and combiner 212which receives input r_(m,chk) ⁽¹⁾ from m-stage intra-CHk BIC1 201,input r_(m,ch1) ⁽¹⁾ from m-stage intra-CH1 BIC1 202, input r_(m,ch1) ⁽²⁾from m-stage intra-CH1 BIC2 205, and input r_(m,chk) ⁽²⁾ from m-stageintra-CHk BIC2 206. apply EQU coefficients and combiner 212 alsoreceived input from EQU1 COEFF 209 and EQU2 COEFF 210. apply EQUcoefficients and combiner 212 then processes the inputs and outputsr_(pi), r_(ch1), and r_(chk) to CPICH demod 213, intra-CH1 demod 214,and inta-CHk demod 215, respectively.

In one embodiment, in system 200 the received high-speed downlink packetaccess (HSDAP) signal of the serving cell includes the synchronousintra-cell channels and the asynchronous inter-cell interferences. TheHSDAP received signal on the p^(th) branch on symbol level is simplifiedas:

$\begin{matrix}{r^{(p)} = {\sum\limits_{k = 0}^{K - 1}{\sum\limits_{{ch} = 0}^{{CH} - 1}{\sum\limits_{l = 0}^{L - 1}{h_{k,{ch},l}^{(p)}d_{k,{ch},l}{\sum\limits_{n = 0}^{N_{k,{ch}} - 1}{SC}_{{ch},{n + l}}}}}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

where SC is the equivalent spreading code combined by the scramblingcode and the orthogonal spreading code. The scrambling codes are used toidentify cells, indexed by k, and in each cell the intra-cell channelsare identified by orthogonal spreading code, indexed by ch. FIG. 2demonstrates a single input, multiple outputs (SIMO) system where thereare two receiving branches. The received signal on each branch ismodeled first as passing to a multi-path channel and the channel h isassumed not changing for the duration of a symbol l is used to index themulti-path. Then ISI will be removed by the equalizer after themulti-stage blind interference cancellation. The equalizer coefficientswill be obtained from common pilot channel (CPICH) and applied on allintra-channels.

Turning next to FIG. 3 which illustrates a block diagram of a system 300for implementing blind interference cancellation on each channel,according to one embodiment of the present invention. In one embodiment,system 300 includes a BIC state 1 301, which includes a covariancematrix estimator R_(r) 302, a desired multipath signal matrix estimatorR_(ch) 303, a max interference vector and matrix on subspace estimator304, an interference on signal space reconstructor 305, and an excessivecancellation detector 306.

The outputs R_(r,1) and r₁ from BIC state 1 301 are received by a BICstage 2 308 and then outputs R_(r,2) to R_(r,m-1) and r₂ to r_(m-1) arereceived by BIC stage m 309. Then, BIC stage m 309 outputs r_(m).

In one embodiment, the following the multi stage blind interferencecancellation block may be implemented using system 300 in FIG. 3. Thereceived signal vector with a length of N_(ch) chips at symbol i isnoted as r(i), where N_(ch) usually is the spreading factor of thechannel. Note that the maximum number of interference components may bedetected and cancelled usually meets the following relations:N_(ch)≧2K_(a)+K_(s)+1, where K_(a) is the total number of asynchronousinterference items and K_(s) is the total number of synchronousinterference items. The receiving signal covariance matrix can beestimated directly from the received signal sample or channelestimation. Equation 2 is a typical sample estimation with theestimation window size of N:

$\begin{matrix}{{R_{r}(i)} = {\frac{1}{N}{\sum\limits_{j = {{i^{*}N_{ch}} - N + 1}}^{i^{*}N_{ch}}{{r(j)}{r^{H}(j)}}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

The desired multipath signal matrix is estimated as:

$\begin{matrix}{{{\hat{R}}_{ch}(i)} = {\sum\limits_{l = 0}^{L - 1}{{{{\hat{h}}_{l}(i)}}^{2}{{SC}_{ch}\left( {{iN}_{ch} - l} \right)}{{SC}_{ch}^{H}\left( {{iN}_{ch} - l} \right)}}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

where H is Hermitian conjugation operation, the channel estimation onsymbol level ĥ can simply use the finger output of RAKE receiver and isnot the key focus of this invention. Dropping the symbol index i forsimplification, the interference covariance matrix can be obtained byremoving the desired signal as:

{circumflex over (R)} _(r,0) ={circumflex over (R)} _(r) −{circumflexover (R)} _(ch)   Equation 4

The mean energy (ME) of the received signal r on the N_(ch)-dimensionalvector space u is defined as ME(u)=E{(r^(H)u)²}, where u^(H)u=1.According to linear algebra theory, the eigenvalue λ and thecorresponding eigenvector v is the necessary condition to maximize ME(v)as R_(r)v=λv. For a set of eigenvalue and the corresponding eigenvectorof R_(r), ME(V)=V^(H)R_(r)V=D , where D=diag{λ_(n)}, n=0 . . . N_(ch)−1and V is the Eigen-matrix consist of N_(ch) orthogonal Eigenvectors.Therefore the eigenvector corresponding to the maximum eigenvalue is thevector that maximizes the mean energy on it. The next step is to removethe maximum interference contribution in the interference covariancematrix in stage m by applying this subspace theory as:

{circumflex over (R)} _(r,m) ={circumflex over (R)} _(r,m-1)−λ_(max)^((m)) v _(max) ^((m)) v ^(H) ^((m)) ^(max)   Equation 5

To find the maximum eigenvalue λ_(max) and the corresponding Eigenvectorv_(max). The Eigen-decomposition in full is not needed. Instead aniterative solution like power method (PM) can be used. The iterationalgorithm to obtain the maximum eigenvalue λ_(max) and the correspondingEigenvector v_(max) is not the key topic in this invention. Theexcessive cancellation detector in stage m can be implemented by athreshold, which is associated with the signal-to-noise ratio and thecorrelation feature of the combined spreading code, check as:

w _(m)=(SC_(ch) ^(H) v _(max) ^((m))>Threshold)?0:1   Equation 6

The cleaner signal will feed to the next stage when w_(m)=1 else stop.

{circumflex over (r)} _(m) =r _(m-1) −w·(r _(m-1) ^(H) v _(max)^((m)))v_(max) ^((m))   Equation 7

where r₀=r.

In UTRA-FDD downlink, the CPICH can be used to calculate the channelequalizer coefficients and the coefficients will apply on each channelafter performing multi-stage blind interference cancellation on eachchannel to remove ISI. The equalizer can be implemented in time domainor frequency domain based on the signal covariance matrix estimation andis not the main focus in this invention.

Referring next to FIG. 4 which illustrates a system 400 for implementingmulti-channel/multi-stage blind interference cancellation, according toone embodiment of the present invention. In one embodiment, system 400includes a J^(th) Channel Multi-Stage Blind Interference Cancellation A401, a 1^(st) Channel Multi-Stage Blind Interference Cancellation A 402,and a channel estimation block A 405, which each receive input {r1}.Further, a J^(th) Channel Multi-Stage Blind Interference Cancellation B404, a 1^(st) Channel Multi-Stage Blind Interference Cancellation B 403,and a channel estimation block B 406, each receive input {ri},

Channel estimation block A 405 provides input {h11} to 1^(st) ChannelMulti-Stage Blind Interference Cancellation A 402 and input {h1} toJ^(th) Channel Multi-Stage Blind Interference Cancellation A 401.Further, channel estimation block B 406 provides input {hi1} to 1^(st)Channel Multi-Stage Blind Interference Cancellation B 403 and input{hij} to J^(th) Channel Multi-Stage Blind Interference Cancellation B404.

System 400 further includes a scrambling spreading code generator 407which provides input {sc11} to 1^(st) Channel Multi-Stage BlindInterference Cancellation A 402 and input {sc1 j} to J^(th) ChannelMulti-Stage Blind Interference Cancellation A 401, as well as input{sci1} to 1^(st) Channel Multi-Stage Blind Interference Cancellation B403 and input {scij} to J^(th) Channel Multi-Stage Blind InterferenceCancellation B 404, scrambling spreading code generator 407 alsoprovides input to a demod block 408, and demod block 408 outputs {d1 todj}.

Furthermore, J^(th) Channel Multi-Stage Blind Interference CancellationA 401 outputs {r1 j} to a J^(th) Channel Combiner 410 and J^(th) ChannelMulti-Stage Blind Interference Cancellation B 404 also outputs {rij} toJ^(th) Channel Combiner 410. 1^(st) Channel Multi-Stage BlindInterference Cancellation A 402 outputs {r11} to 1^(st) Channel Combiner409 and 1^(st) Channel Multi-Stage Blind Interference Cancellation B 403outputs {ri1} to 1^(st) Channel Combiner 409. Further, 1^(st) ChannelCombiner 409 and J^(th) Channel Combiner 410 provide output to demodblock 408.

FIGS. 5A and 5B illustrates a method 500 for implementing blindinterference cancellation, according to one embodiment of the presentinvention. In one embodiment, method 500 may be implemented using anyone of systems 100, 200, 300, or 400. At process block 501, a signalvector is received and saved. The signal vector is then input into a 1ststage blind interference cancellation module. At process block 502, acovariance matrix is received and processed.

At process block 503, a channel estimation for a desired signal isperformed and a covariance matrix for the desired signal is processed(process block 504). At process block 505, an interference matrix isprocessed by subtracting the output of the processing of the covariancematrix for the desired signal. Then, the maximum eigenvalue and itseigenvector of the interference matrix is determined (process block506).

At process block 507, the strongest interference based on the determinedeigenvector is generated to rebuild the 2^(nd) item in Equation 7. Thestrongest interference covariance matrix is determined based on theeigenvalue and its eigenvector for the 2^(nd) item in Equation 5(process block 508). Then, at process block 509, a threshold is checkedagainst the correlation between the desired signal and the eigenvectorresults.

If the threshold is not exceeded, then the process moves to point ‘A’,otherwise the process moves to point ‘B’ and on to process block 515.Turning now to FIG. 5B from point ‘A’, at process block 511, a cleanersignal vector is obtained by subtracting the results of process block507 from the input to the stage vector.

A determination is made whether the number of stages has reached thenumber of the covariance matrix dimension (decision block 512). If thenumber of stages has not reached the number of the covariance matrixdimensions, then at process block 513, the remaining interferencecovariance matrix is processed by removing the results from processblock 508. At process block 514, the next stage BIC is stared with twoinputs from process blocks 511 and 513. Then, the process returns toprocess block 506 via point ‘C’. Otherwise, the process proceeds topoint ‘B’ and process block 515. At process block 515, the outputs fromprocess block 511 from each antenna for each channel are combined. Atprocess block 516, the outputs of process block 515 for each channel aredispread.

One implementation of the method 500 includes calculating (2×2)covariance matrix (it should be noted that the larger the dimension ofthe covariance matrix, the more interference items can be detected andfurther cancelled, and usually the size of the covariance matrix isequivalent to the length of each CDMA channel's spreading code).

In one embodiment, the desired signal's signature code in one symbol forthe desired user is:

s₀=[1 1]^(T),

-   -   the interference's item is:

s₁=[1 −1]^(T)

-   -   the covariance matrix for the received signal is:

${R = \begin{bmatrix}2.1 & 0 \\0 & 0.1\end{bmatrix}},$

-   -   with a variance of normal additive noise is estimated at 0.1.    -   the covariance matrix for the desired user is estimated        perfectly as:

$R_{0} = \begin{bmatrix}0.5 & 0.5 \\0.5 & 0.5\end{bmatrix}$

-   -   the covariance matrix after being removed, the desired user's        contribution (interference and noise) will be:

$R_{r\; 0} = {\begin{bmatrix}1.6 & {- 0.5} \\{- 0.5} & {- 0.4}\end{bmatrix}.}$

-   -   the max Eigenvalue and corresponding Eigenvector will be:

λ_(max)=1.718 and ν_(max)=[−0.9732 0.2298]^(T).

The rebuild interference ŝ₁=[1.9337 0.4565] and the cleaner signal afterinterference remove will be ŝ₀=[0.0915 0.3875]^(T) and the estimatedsymbol will be detected as 1 as expected,

Turning now to FIG. 6 which illustrates a system 600 of a receiver withan interference cancellation and equalizer, according to one embodimentof the present invention. In one embodiment, system 600 includes similarelements of FIG. 1 with the Interference and Cancellation EqualizerBlock A 601, Interference and Cancellation Equalizer Block B 602, andCombine and Demod Block 603, which replace Blind InterferenceCancellation Block A 112, Blind Interference Cancellation Block B 113,and RF block 114,

Turning next to FIG. 7 which illustrates a system 700 for implementingmulti-channel interference cancellation and equalizer, according to oneembodiment of the present invention. In one embodiment, system 700includes the elements of FIG. 4 with a Reference Channel EqualizerCoefficient Estimation 701, a finite impulse response (FIR) Filter Bank702, and Demod Block 703, which replace Demod Block 408, 1st ChannelCombiner 409, and J^(th) Channel Combiner 410.

FIG. 8 illustrates a system 800 for implementing a self-trackingmulti-channel equalizer based on a pilot channel, according to oneembodiment of the present invention. In one embodiment, system 800includes a storage block A 801 which receives input {r1} or {r11}.System 800 further includes a storage block B 802 which receives inputor {ri} or {ri1}. System 800 further includes a Channel Estimation withFraction Chip Level Resolution A 803 and Channel Estimation withFraction Chip Level Resolution B 804, which receives input from storageblock A 801 and storage block B 802, respectively.

System 800 further includes a Scrambling Spreading Code Generator 805which sends output {sc1} to Channel Estimation with Fraction Chip LevelResolution A 803 and output {cs1} to Channel Estimation with FractionChip Level Resolution B 804. Furthermore, Channel Estimation withFraction Chip Level Resolution A 803 receives input {t11} from anEqualizer Control Engine 806 and Channel Estimation with Fraction ChipLevel Resolution B 804 receives an input {ti1} from Equalizer ControlEngine 806.

System 800 further includes an Equalizer Coefficient Estimator 807 whichreceives input {hlk} from Channel Estimation with Fraction Chip LevelResolution A 803 and input {hik} from Channel Estimation with FractionChip Level Resolution B 804. Further, Equalizer Coefficient Estimator807 outputs c1 to cn.

Referring now to FIG. 9 which illustrates a method 900 for combiningsymbol level interference cancellation and chip level channel equalizer,according to one embodiment of the present invention. At process block901, the signal vectors are saved on the reference channel afterinterference cancellation or with the resolution of the fraction chip. Acovariance matrix is then received and processed (process block 902).

At process block 903, the received and processed Covariance matrix isused, and the equalizer tap coefficient and tap energy are calculatedand saved (process block 904). At decision block 905, a determination ismade whether additional start time positions are in start window. Ifthere are additional start time positions in the start window, then theprocess returns to process block 902. Otherwise the process continues toprocess block 906, which analyzes the channel characters and the outputsignal-to-noise ratio (SNR) in the control engine to decide the timeposition in the fraction chip.

At process block 907, the input signal vector window time position maybe changed based on results of process block 906. Then, the channelestimation start time position is changed based on results of processblock 906 (process block 908). At process block 909, the demodulationtime position is determined based on process block 906. Then, adetermination for a set of time positions the equalizer tap coefficientsis made (process block 910). At process block 911, the equalizer tapcoefficients are applied on each channel signal vector for a desireduser.

Referring next to FIG. 10 which illustrates a method 1000 fordynamically selecting the interference cancellation and channelequalizer, according to one embodiment of the present invention. Atdecision block 1001, a determination is made whether valid interferencecomponents are detected on the reference channel. If valid interferencecomponents are detected on the reference channel, then the process movesto process block 1004, otherwise the process continues to process block1002.

At process block 1002, blind interference cancellation on each channelis processed. At process block 1003, the output SNR of the BIC on eachchannel are estimated. Then, the channel equalizer coefficients on thereference channel are estimated. At process block 1005, the timingposition in response to the output of process block 1003 is adjusted.

At process block 1006, the equalizer coefficients on all channels areapplied and the output SNR of equalizer on each channel are estimated(process block 1007). At decision block 1008, a determination of whetherthe SNR equalization output is improved. If the SNR equalization outputis improved, then the output of equalizer to the demodulation isconnected. Otherwise, the output of interference to the demodulation isconnected.

FIG. 11 is a block diagram illustrating an exemplary system 1100 inwhich embodiments of the present invention may be implemented. Forexample, the system 1100 can be a wireless communication device, aportable computer with wireless communication interface, abase station,and/or others. The system 1100 is shown comprising hardware elementsthat may be electrically coupled via a bus 1190. The hardware elementsmay include one or more central processing units 1110, one or more inputdevice(s) 1120 (e.g., keypad, etc.), and one or more output device(s)1130 (e.g., a display device, a printer, etc.). The system 1100 may alsoinclude one or more storage device(s) 1140. By way of example, storagedevice(s) 1140 may be disk drives, optical storage devices, asolid-state storage device such as a random access memory (“RAM”) and/ora read-only memory (“ROM”), which can be programmable, flash-updateable,and/or the like. The system 1100 is adapted to performed interferencecancellation in real time. For example, the interference cancellation isperformed with a latency of less than 2 ms.

The system 1100 may additionally include a computer-readable storagemedia reader 1150. The system 1100 includes a communication system 1160(e.g., a modem, a network card (wireless or wired), an infra-redcommunication device, Bluetooth™ device, cellular communication device,etc.), and working memory 1180, which may include RAM and ROM devices asdescribed above. For example, the communication system 1160 comprises awireless communication interface compatible with CDMA standard. In someembodiments, the computer system 1100 may also include a processingacceleration unit 1170, which can include a digital signal processor, aspecial-purpose processor, and/or the like.

The computer-readable storage media reader 1150 can further be connectedto a computer-readable storage medium, together (and, optionally, incombination with storage device(s) 1140) comprehensively representingremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containingcomputer-readable information. The communication system 1160 may permitdata to be exchanged with a wireless network, system, computer, and/orother component described above.

The system 1100 may also comprise software elements, shown as beingcurrently located within a working memory 1180, including an operatingsystem 1188 and/or other code 1184. It should be appreciated thatalternate embodiments of a computer system 1100 may have numerousvariations from that described above. For example, customized hardwaremight also be used and/or particular elements might be implemented inhardware, software (including portable software, such as applets), orboth. Furthermore, connection to other computing devices such as networkinput/output and data acquisition devices may also occur.

Software of system 1100 may include code 1184 for implementing any orall of the functions of the various elements of the architecture asdescribed herein. For example, software, stored on and/or executed by acomputer system such as system 1100, can provide the functionalityand/or other components of the invention such as those discussed above.Methods implementable by software on some of these components have beendiscussed above in more detail.

Merely by way of example, FIG. 12 illustrates a schematic diagram of asystem 1200 that can be used in accordance with one set of embodiments.The system 1200 can include one or more user devices 1205. The userdevices 1205 can be general purpose personal computers (including,merely by way of example, laptop computers running any appropriateflavor of Microsoft Corp.'s Windows™ and/or Apple Corp.'s Macintosh™operating systems) and/or workstation computers running any of a varietyof commercially available UNIX™ or UNIX-like operating systems. Theseuser computers 1205 can also have any of a variety of applications,including one or more applications configured to perform methods of theinvention, as well as one or more office applications, database clientand/or server applications, and web browser applications. Alternatively,the user computers 1205 can be any other electronic device, such as athin-client computer, Internet-enabled mobile telephone, and/or personaldigital assistant (PDA), capable of communicating via a wireless network(e.g., the network 1210 described below) and/or displaying andnavigating web pages or other types of electronic documents. Althoughthe exemplary system 1200 is shown with three user devices 1205, anynumber of user computers can be supported. For example, the user device1205 a is a wireless phone, which can be configured to performedinterference cancellation described above.

Certain embodiments of the invention operate in a networked environment,which can include a network 1210. The network 1210 can be any type ofnetwork familiar to those skilled in the art that can support datacommunications using any of a variety of commercially availableprotocols, including without limitation TCP/IP, SNA, IPX, AppleTalk, andthe like. Merely by way of example, the network 1210 can be a local areanetwork (“LAN”), including without limitation an Ethernet network, aToken-Ring network, and/or the like; a wide-area network (WAN); avirtual network, including without limitation a virtual private network(“VPN”); the Internet; an intranet; an extranet; a public switchedtelephone network (“PSTN”); an infrared network; a wireless network,including without limitation a network operating under any of the IEEE802.11 suite of protocols, the Bluetooth™ protocol known in the art,and/or any other wireless protocol; and/or any combination of theseand/or other networks. In various embodiments, the network 1210 is awireless communication network. For example, the network 1210 isconfigured to communicate using wireless communication protocols such asCDMA, GSM, and/or others.

Embodiments of the invention can include one or more server computers1215. Each of the server computers 1215 may be configured with anoperating system, including without limitation any of those discussedabove, as well as any commercially (or freely) available serveroperating systems. Each of the servers 1215 may also be running one ormore applications, which can be configured to provide services to one ormore user devices 1205 and/or other server computers 1215.

Merely by way of example, one of the server computers 1215 may be a webserver, which can be used, merely by way of example, to process requestsfor web pages or other electronic documents from user computers 1205.The web server can also run a variety of server applications, includingHTTP servers, FTP servers, CGI servers, database servers, Java™ servers,and the like. In some embodiments of the invention, the web server maybe configured to serve web pages that can be operated within a webbrowser on one or more of the user computers 1205 to perform methods ofthe invention.

The server computers 1215, in some embodiments, might include one ormore application servers, which can include one or more applicationsaccessible by a client running on one or more of the user computers 1205and/or other server computers 1215. Merely by way of example, the servercomputers 1215 can be one or more general purpose computers capable ofexecuting programs or scripts in response to the user computers 1205and/or other server computers 1215, including without limitation webapplications (which might, in some cases, be configured to performmethods of the invention). Merely by way of example, a web applicationcan be implemented as one or more scripts or programs written in anysuitable programming language, such as Java™, C, C#™ or C++, and/or anyscripting language, such as Perl, Python, or TCL, as well ascombinations of any programming/scripting languages. The applicationserver(s) can also include database servers, including withoutlimitation those commercially available from Oracle™, Microsoft™.Sybase™, IBM™ and the like, which can process requests from clients(including, depending on the configuration, database clients, APIclients, web browsers, etc.) running on a user computer 1205 and/oranother server computer 1215. In some embodiments, an application servercan create web pages dynamically for displaying the information inaccordance with embodiments of the invention. Data provided by anapplication server may be formatted as web pages (comprising HTML,Javascript, etc., for example) and/or may be forwarded to a usercomputer 1205 via a web server (as described above, for example).Similarly, a web server might receive web page requests and/or inputdata from a user computer 1205 and/or forward the web page requestsand/or input data to an application server. In some cases, a web servermay be integrated with an application server.

In accordance with further embodiments, one or more server computers1215 can function as a file server and/or can include one or more of thefiles (e.g., application code, data files, etc.) necessary to implementmethods of the invention incorporated by an application running on auser computer 1205 and/or another server computer 1215. Alternatively,as those skilled in the art will appreciate, a file server can includeall necessary files, allowing such an application to be invoked remotelyby a user computer 1205 and/or server computer 1215. It should be notedthat the functions described with respect to various servers herein(e.g., application server, database server, web server, file server,etc.) can be performed by a single server and/or a plurality ofspecialized servers, depending on implementation-specific needs andparameters.

In certain embodiments, the system can include one or more database(s)1220. The location of the database(s) 1220 is discretionary. Merely byway of example, a database 1220 a might reside on a storage medium localto (and/or resident in) a server computer 1215 a (and/or a user computer1205). Alternatively, a database 1220 b can be remote from any or all ofthe computers 1205, 1215, so long as the database can be incommunication via the network 1210) with one or more of these. In aparticular set of embodiments, a database 1220 can reside in astorage-area network (“SAN”) familiar to those skilled in the art.(Likewise, any necessary files for performing the functions attributedto the computers 1205, 1215 can be stored locally on the respectivecomputer and/or remotely, as appropriate.) In one set of embodiments,the database 1220 can be a relational database, such as an Oracle™database, that is adapted to store, update, and retrieve data inresponse to SQL-formatted commands. The database might be controlledand/or maintained by a database server, as described above, for example.

The invention has now been described in detail for the purposes ofclarity and understanding. However, it will be appreciated that certainchanges and modifications may be practiced within the scope of theappended claims.

What is claimed:
 1. A method for canceling interference in wirelesscommunication, the method comprising: receiving wireless code divisionmultiple access (CDMA) communication signals using one or more antennasvia a first communication channel via a second communication channel;determining a set of known characteristics associated with communicationsignals from the first communication channel, the first set ofcharacteristics comprising a first signal strength, a firstsynchronization information, and an first channel identificationinformation; determining an aggregate signal matrix based on noise andcommunication signals received from the communication channel and thesecond communication channel; determining a covariance matrix associatedwith the aggregate signal value; determining a reference signal matrixbased on the set of known characteristics; calculating an interferencematrix by subtracting the reference signal matrix from the covariancematrix; determining a maximum eigenvalue and an eigenvectorcorresponding to the maximum eigenvalue for with the interferencematrix; generating an interference estimation using the maximumeigenvalue and the eigenvector; and removing the interference estimationfrom the communication signals.
 2. The method of claim 1, furthercomprising receiving communication signals via a third communicationchannel.
 3. The method of claim 2, wherein the aggregate signal matrixis further based on the communication signals via the thirdcommunication channel.
 4. The method of claim 1, further comprisingperforming recursive calculations with a threshold condition todetermine the maximum eigenvalue.
 5. The method of claim 2, wherein thethreshold is determined based on a desired signal-to-noise ratio and thecorrelation feature of the designed signature code.
 6. The method ofclaim 1, wherein the channel identification information comprises asignature (spreading) code, scrambling code, in orthogonal code, ornon-orthogonal.
 7. The method of claim 1, wherein the reference signalmatrix is associated with covariance values of the set of knowncharacteristics.
 8. The method of claim 1, wherein the wirelesscommunication CDMA signals includes interference signals.
 9. A systemfor canceling interference in wireless communication, the systemcomprising: a storage device; and a processor in communication with thestorage device, wherein the storage device has sets of instructionsstored thereon which, when executed by the processor, cause theprocessor to: receive wireless code division multiple access (CDMA)communication signals using one or more antennas via a firstcommunication channel via a second communication channel, determine aset of known characteristics associated with communication signals fromthe first communication channel, the first set of characteristicscomprising a first signal strength, a first synchronization information,and an first channel identification information, determine an aggregatesignal matrix based on noise and communication signals received from thecommunication channel acid the second communication channel, determine acovariance matrix associated with the aggregate signal value, determinea reference signal matrix based on the set of known characteristics,calculate an interference matrix by subtracting the reference signalmatrix from the covariance matrix, determine a maximum eigenvalue and aneigenvector corresponding to the maximum eigenvalue for with theinterference matrix, generate an interference estimation using themaximum eigenvalue and the eigenvector, and remove the interferenceestimation from the communication signals.
 10. The system of claims 9,wherein the sets of instruction when further executed by the processor,cause the processor to perform recursive calculations with a thresholdcondition to determine the maximum eigenvalue.
 11. The system of claim10, wherein the threshold is determined based on a desiredsignal-to-noise ratio and the correlation feature of the designedsignature code.
 12. The system of claim 9, wherein the channelidentification information comprises a signature (spreading) code,scrambling code, in orthogonal code, or non-orthogonal.
 13. The systemof claim 9, wherein the reference signal matrix is associated withcovariance values of the set of known characteristics.
 14. The system ofclaim 9, wherein the wireless communication CDMA signals includesinterference signals.
 15. A computer-readable medium for updating userassistance content, having sets of instructions stored thereon which,when executed by a computer, cause the computer to: receive wirelesscode division multiple access (CDMA) communication signals using one ormore antennas via a first communication channel via a secondcommunication channel; determine a set of known characteristicsassociated with communication signals from the first communicationchannel, the first set of characteristics comprising a first signalstrength, a first synchronization information, and an first channelidentification information; determine an aggregate signal matrix basedon noise and communication signals received from the communicationchannel and the second communication channel; determine a covariancematrix associated with the aggregate signal value; determine a referencesignal matrix based on the set of known characteristics; calculate aninterference matrix by subtracting the reference signal matrix from thecovariance matrix; determine a maximum eigenvalue and an eigenvectorcorresponding to the maximum eigenvalue for with the interferencematrix; generate an interference estimation using the maximum eigenvalueand the eigenvector; and remove the interference estimation from thecommunication signals.
 16. The computer-readable medium of claim 15,wherein the sets of instruction when further executed by the computer,cause the computer to perform recursive calculations with a thresholdcondition to determine the maximum eigenvalue.
 17. The computer-readablemedium of claim 16, wherein the threshold is determined based on adesired signal-to-noise ratio and the correlation feature of thedesigned signature code.
 18. The computer-readable medium of claim 15,wherein the channel identification information comprises a signature(spreading) code, scrambling code, in orthogonal code, ornon-orthogonal.
 19. The computer-readable medium of claim 15, whereinthe reference signal matrix is associated with covariance values of theset of known characteristics.
 20. The computer-readable medium of claim15, wherein the wireless communication CDMA signals includesinterference signals.